MatterGPT 图形界面发布及实用性功能升级_bugFix_1

改进了监控脚本 utils.py，将任务检测与监控机制从依赖 Slurm-Torque 转为直接使用 Slurm 原生命令 squeue，增强了兼容性和稳定性。

MatterGPT 图形界面发布及实用性功能升级！

MatterGPT 图形界面发布啦！基于 Gradio 打造，界面简洁实用，轻松上手，快来 GitHub 下载最新repo体验吧！🎉
我们还完善了本地安装模式，实现了本地安装与 Docker 安装的统一使用体验，让新材料设计更加便捷高效。
新增亮点功能：
任务管理系统： 本地安装模式内置简洁任务管理系统，默认支持 8 线程并行计算，充分发挥硬件性能。
早停机制 (Early Stop)： 训练过程中如果验证损失不再下降，将自动提前停止训练，节省时间与资源。
多目标逆向设计： 原生支持 1-N 个性质的逆向设计需求，所有参数均已暴露，真正实现零代码操作，无需修改 train.py 等核心代码。
结构新颖性检查优化： 分离 SLICES 解码与结构新颖性检查功能，并支持数据文件分片处理，大幅提升计算效率。
快来体验 MatterGPT 的强大功能，让 AI 赋能材料科学研究吧！ 🚀

MatterGPT and SLICES v2.2.0

MatterGPT and SLICES v2.2.0 Latest

v2.2.0

🐛 Bug Fixes | 问题修复

修复了MatterGPT/eform/2_inverse_eform_novelty/workflow/script.py的自动清空结果的问题。

Fixed the issue of automatic result clearing in MatterGPT/eform/2_inverse_eform_novelty/workflow/script.py.

v2.1.0

🐛 Bug Fixes | 问题修复
Memory Leak Fix | 内存泄漏修复 🔄 Crystal Reconstruction Routine of MatterGPT | 晶体重建流程
修复MatterGPT晶体结构重建流程的TensorFlow 内存泄漏问题
大幅提高晶体重建成功率
优化m3gnet/chgnet优化过程中的内存管理
增强长时间运行的稳定性
Fixed TensorFlow memory leak in MatterGPT's Crystal Reconstruction Routine
Improved crystal reconstruction success rate
Optimized memory handling during tensor operations
Enhanced long-running process stability

MatterGPT and SLICES v2.0.0

SLICES 2.0.0 Changelog

1. Introduced MatterGPT model, a GPT2-based large language model for solid-state materials, specifically designed for inverse design of solid-state materials. It excels in multi-property inverse design. For details, see [ paper ]. Added three new Jupyter tutorials (2.1, 2.2, 2.3) to help users get started with experiments. Add an online demo for MatterGPT [Online Demo for MatterGPT].
2. Updated SLI2Cry algorithm: Replaced M3GNet IAP with CHGNet IAP in the third step, further improving invertibility and enhancing SLICES crystal decoding capabilities. Add an online demo for SLICES [Online Demo for SLICES], allowing online conversion between SLICES and CIF.
3. Set CrystalNN as the default crystal chemical bond recognition algorithm for SLICES encoding.
4. Adopted SLICES strategy=4 as the new default encoding method. This encoding can shorten SLICES length by about 40%, effectively reducing computation for large models. Example of the new encoding:
   C C 0 1 ooo 0 1 +oo 0 1 o+o 0 1 oo+
   This simplifies the offset vector from three separate tokens to one token, streamlining the SLICES string.
5. Relocated new version code files to the 'slices' folder. Changed the SLICES library import command from:
   from invcryrep.invcryrep import InvCryRep
   to the more intuitive:
   from slices.core import SLICES
6. Docker image now uses 'python setup.py develop' to install code from the /crystal/slices folder, facilitating SLICES algorithm development. Users can directly modify source code in /crystal/slices, ensuring the docker-called SLICES library is always up-to-date without reinstallation or environment reconfiguration.
7. The Custodian package is utilized for automating DFT verification calculations with VASP, providing error handling and workflow management capabilities.

SLICES 2.0.0 更新日志

1. 引入MatterGPT模型，这是一个基于GPT2的新材料大语言模型，专门为凝聚态材料逆向设计打造，擅长进行多性质逆向设计。详情请参阅 [ 论文 ]。新增三个Jupyter教程（2.1、2.2、2.3），方便用户快速上手逆向设计新材料。 添加了一个在线演示 [材料大模型在线演示].
2. 更新SLI2Cry算法：将第三步的M3GNet IAP替换为CHGNet IAP，进一步提高可逆性，增强SLICES晶体解码能力。添加了一个在线演示 [SLICES编解码在线演示]. 可以在线对SLICES和CIF进行转换以及数据增广。
3. 将CrystalNN设置为SLICES编码使用的默认晶体化学键识别算法。
4. 采用SLICES strategy=4作为新的默认编码方法。这种编码可以缩短SLICES长度约40%，有效降低大模型的计算量。新型编码示例：
   C C 0 1 ooo 0 1 +oo 0 1 o+o 0 1 oo+
   这种方法将偏移矢量从三个独立的token简化为一个token，有效精简SLICES字符串。
5. 将新版本的代码文件放在slices文件夹中。调用SLICES库的命令从：
   from invcryrep.invcryrep import InvCryRep
   变为更加直观的：
   from slices.core import SLICES
6. Docker镜像现在使用'python setup.py develop'安装/crystal/slices文件夹中的代码，方便进行SLICES算法的开发。用户可以直接在/crystal/slices中修改源代码，确保docker调用的SLICES库始终是最新版本，无需重新安装或配置环境。
7. DFT计算使用custodian进行自动化纠错，提高计算成功率。

New Tutorials base on Jupyter Notebooks

We have developed new tutorials for SLICES based on Jupyter notebooks. This comprehensive overhaul makes the tutorials much easier to use. 🧠⛏️💎

v1.4.4

Installing SLICES is now easier than ever with just one command: "pip install slices".

If you're in China and want to speed up the download, you can use this command instead: "pip install slices -i https://pypi.tuna.tsinghua.edu.cn/simple".

Please note that this installtion method is intended for Linux operating systems like Ubuntu and CentOS. Unfortunately, SLICES is not compatible with Windows due to the modified XTB binary was compiled on Linux.

Once the installation is complete, you can immediately explore SLICES' capabilities. Test the encoding/decoding and data augmentation/canonicalization functions using the *.py files located in the /examples directory. It's a quick and easy way to get started!

v1.4

Major Revision Data

v1.3.1

modify duplicate fix and check functions

v1.3

Add check_SLICES and get_canonical_SLICES

v1.2

Update data augmentation function.